An extremal inequality motivated by multiterminal information-theoretic problems

Tie Liu; Pramod Viswanath

doi:10.1109/TIT.2007.894680

An extremal inequality motivated by multiterminal information-theoretic problems

Tie Liu
, Pramod Viswanath

Research output: Contribution to journal › Article › peer-review

158 Scopus citations

Abstract

We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problems. As a corollary, this inequality yields a generalization of the classical entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing the differential entropy of the sum of two dependent random variables.

Original language	English (US)
Pages (from-to)	1839-1851
Number of pages	13
Journal	IEEE Transactions on Information Theory
Volume	53
Issue number	5
DOIs	https://doi.org/10.1109/TIT.2007.894680
State	Published - May 2007
Externally published	Yes

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Library and Information Sciences

Keywords

Differential entropy
Distributed source coding
Entropy- power inequality (EPI)
Fisher information
Vector Gaussian broadcast channel

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10.1109/TIT.2007.894680

Cite this

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abstract = "We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problems. As a corollary, this inequality yields a generalization of the classical entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing the differential entropy of the sum of two dependent random variables.",

keywords = "Differential entropy, Distributed source coding, Entropy- power inequality (EPI), Fisher information, Vector Gaussian broadcast channel",

author = "Tie Liu and Pramod Viswanath",

note = "Funding Information: Manuscript received April 21, 2006; revised November 7, 2006. This work was supported by the National Science Foundation under Grant 0237549. The material in this correspondence was presented in part at the IEEE International Symposium on Information Theory, Seattle, WA, July 2006. T. Liu is with the Department of Electrical and Computer Engineering, Texas A\&M University, College Station, TX 77843 USA (e-mail: [email protected]. edu). P. Viswanath is with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA (e-mail: [email protected]). Communicated by Y. Steinberg, Associate Editor for Shannon Theory. Digital Object Identifier 10.1109/TIT.2007.894680",

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KW - Fisher information

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An extremal inequality motivated by multiterminal information-theoretic problems

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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